Choropleths with geopandas

Visualizando dados geoespaciais em Python

Mary van Valkenburg

Data Science Program Manager, Nashville Software School

chart of sequential colormaps

Visualizando dados geoespaciais em Python

Choropleth with GeoDataFrame.plot()

districts_with_counts.plot(column = 'school_density', legend = True)
plt.title('Schools per decimal degrees squared area')
plt.xlabel('longitude')
plt.ylabel('latitude');

choropleth of school densities using viridis colormap

Visualizando dados geoespaciais em Python

Choropleth with GeoDataFrame.plot()

districts_with_counts.plot(column = 'school_density', cmap = 'BuGn', edgecolor = 'black', legend = True)
plt.title('Schools per decimal degrees squared area')
plt.xlabel('longitude')
plt.ylabel('latitude');

choropleth of school densities with blue-green colormap

Visualizando dados geoespaciais em Python

Area in Kilometers Squared

# starting CRS
print(school_districts.crs)

epsg:4326

# convert to EPSG 3857
school_districts = school_districts.to_crs(epsg = 3857)
print(school_districts.crs)

epsg:3857
Visualizando dados geoespaciais em Python

Area in Kilometers Squared

# define a variable for m^2 to km^2
sqm_to_sqkm = 10**6

school_districts['area'] = school_districts.area / sqm_to_sqkm
school_districts.head(2)

district    geometry                             area
1          (POLYGON ((-965.055 4353528.766...    563.134380
3          (POLYGON ((-965.823 4356392.677...    218.369949
Visualizando dados geoespaciais em Python
# change crs back to 4326
school_districts = school_districts.to_crs(epsg = 4326)
print(school_districts.crs)

epsg:4326

print(school_districts.head(2))

district      geometry                       area
1             (POLYGON ((-86.771 36.383...   563.134380
3             (POLYGON ((-86.753 36.404...   218.369949

# spatial join to get districts that contain schools
schools_in_districts = gpd.sjoin(school_districts, schools_geo, predicate = 'contains')
Visualizando dados geoespaciais em Python
# aggregate to get counts
school_counts = schools_in_districts.groupby(['district']).size()

# convert school_counts to a df 
school_counts_df = school_counts.to_frame()
school_counts_df = school_counts_df.reset_index(level=0)
school_counts_df.columns = ['district', 'school_count']

# merge
districts_with_counts = pd.merge(school_districts,
                                 school_counts_df, on = 'district')

districts_with_counts.head(1)

district  geometry                       area       school_count
1         (POLYGON ((-86.771 36.383..    563.134380    30
Visualizando dados geoespaciais em Python

Calculating school density

# create school_density
districts_with_counts['school_density'] = districts_with_counts.apply(
    lambda row: row.school_count/row.area, axis = 1)

# plot it
districts_with_counts.plot(column = 'school_density', cmap = 'BuGn', 
                           edgecolor = 'black', legend = True)
plt.title('Schools per kilometers squared')
plt.xlabel('longitude')
plt.ylabel('latitude')
plt.show();
Visualizando dados geoespaciais em Python

choropleth of school densities per kilometer squared

Visualizando dados geoespaciais em Python

Let's practice!

Visualizando dados geoespaciais em Python

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